As users enter text on a computing device, such as a phone, some technologies provide suggestions on a word they may be trying to type or a word that may come next in the sentence. To generate word suggestions, there are a number of technologies that are designed to identify relevant words. For instance, some models analyze common sequences of words in a data set, and when a specific word of a sequence is entered in a device, a word that typically follows the specific word is suggested to the user. In one example, if a user enters the word “heart,” most systems using this sequence-based technology would suggest the word “attack” since samples sets may indicate that sequence of words.
Other technologies may use user personalization data to generate word suggestions. For example, a device may store text data from a user's input. The device may then analyze words or sequences of words that are frequently used by a particular user to suggest words to a user.
Although existing technologies provide word suggestions, there is room for improvement. For example, existing technologies are unaware of the context of the user's input and/or other text related to the input. The analysis of word sequences simply cannot interpret a broader meaning to provide a contextually relevant suggestion.
It is with respect to these and other considerations that the disclosure made herein is presented.